import numpy as np
import requests
from .emma_mdp import EmmaAction, EmmaContext, EmmaState
from .emma_identity import get_system_prompt


class OllamaBackend:
    """
    LLM-backend mot Ollama (llama3.2 eller annen lokal modell).
    Injiserer Emmas identitet i alle system-prompts.
    """

    def __init__(self, model: str = "llama3.2", embed_model: str = "nomic-embed-text",
                 base_url: str = "http://localhost:11434"):
        self.model = model
        self.embed_model = embed_model
        self.base_url = base_url
        self.system_prompt = get_system_prompt()

    def embed(self, text: str) -> np.ndarray:
        r = requests.post(f"{self.base_url}/api/embeddings",
                          json={"model": self.embed_model, "prompt": text}, timeout=30)
        r.raise_for_status()
        return np.array(r.json()["embedding"], dtype=np.float32)

    def think(self, ctx: EmmaContext, patterns: list, state: EmmaState) -> str:
        context_hint = "\n".join(str(p.get("action", "")) for p in patterns[:3] if isinstance(p, dict))
        prompt = f"[State: {state.name}]\nPatterns: {context_hint}\nUser: {ctx.userinput}"
        return self._chat(prompt)

    def compress(self, thoughts: list) -> str:
        joined = "\n".join(thoughts)
        return self._chat(f"Komprimér til ett kort sammendragsspørsmål:\n{joined}")

    def choose_action(self, ctx: EmmaContext, thought: str, patterns: list, actions: list) -> EmmaAction:
        names = [a.name for a in actions]
        resp = self._chat(f"Velg én handling fra {names} basert på: {thought[:200]}. Svar kun med handlingens navn.")
        for a in actions:
            if a.name.lower() in resp.lower():
                return a
        return EmmaAction.RESPOND

    def execute_action(self, action: EmmaAction, ctx: EmmaContext) -> dict:
        resp = self._chat(f"[Action: {action.name}] {ctx.userinput}")
        return {"response": resp, "task_completed": True}

    def _chat(self, prompt: str) -> str:
        r = requests.post(
            f"{self.base_url}/api/chat",
            json={
                "model": self.model,
                "messages": [
                    {"role": "system", "content": self.system_prompt},
                    {"role": "user", "content": prompt},
                ],
                "stream": False,
            },
            timeout=60,
        )
        r.raise_for_status()
        return r.json()["message"]["content"]